Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained predict score each single at a time considering rating schema. In order address this issue, we propose reinforcement learning framework that incorporates quadratic weighted kappa as guidance optimize system. Experiment results on benchmark datasets show effectiveness our framework.

Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to smaller learnable student network. Existing KD methods have mainly considered two types of knowledge, namely the individual and relational knowledge. However, these are usually modeled independently while inherent correlations between them largely ignored. It is critical for sufficient learning integrate both reserving their correlation. In this paper, we propose distill novel holistic...

Synchronizing a few-level quantum system is of fundamental importance to the understanding synchronization in deep regime. Whether two-level system, smallest can be synchronized has been theoretically debated for past several years. Here, first time, we demonstrate that qubit indeed an external driving signal by using trapped-ion system. By engineering fully controllable gain and damping processes, ion locked oscillates phase. Moreover, upon tuning parameters signal, observe characteristic...

Yucheng Wang, Bowen Yu, Hongsong Zhu, Tingwen Liu, Nan Limin Sun. Proceedings of the 59th Annual Meeting Association for Computational Linguistics and 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Open Information Extraction (OIE), the task aimed at discovering all textual facts organized in form of (subject, predicate, object) found within a sentence, has gained much attention recently. However, some knowledge-driven applications such as question answering, we often have target entity and hope to obtain its structured factual knowledge for better understanding, instead extracting possible aimlessly from corpus. In this paper, define new task, namely Semi-Open (SOIE), address need....

Equilibrium Optimizer (EO) is a newly developed intelligent optimization algorithm inspired by control volume mass balance models. EO has been proven to have an excellent solution effect on some problems, with the advantages of ease implementation and strong adaptability. However, original disadvantages when solving complex multimodal including immature between exploration exploitation, high probability falling into local optima entrapment, slow rate convergence. In order address these...

It has long been assumed that the sheer number of parameters in large language models (LLMs) drives in-context learning (ICL) capabilities, enabling remarkable performance improvements by leveraging task-specific demonstrations. Challenging this hypothesis, we introduce DEEP-ICL, a novel task Definition Enriched ExPert Ensembling methodology for ICL. DEEP-ICL explicitly extracts definitions from given demonstrations and generates responses through examples. We argue improvement ICL does not...

Abstract Two‐photon excited fluorescence imaging requires high‐performance two‐photon absorption (TPA) active materials, which are commonly intramolecular charge transfer systems prepared by traditional chemical synthesis. However, this typically needs harsh conditions and new methods becoming crucial. In work, based on a collaborative intermolecular (inter‐CT) strategy, three centimeter‐sized organic TPA cocrystals successfully obtained. All exhibit mixed stacking arrangement, can...

There is substantial interest in the use of machine learning (ML)-based techniques throughout electronic computer-aided design (CAD) flow, particularly methods based on deep learning. However, while have achieved state-of-the-art performance several applications, recent work has demonstrated that neural networks are generally vulnerable to small, carefully chosen perturbations their input (e.g. a single pixel change an image). In this work, we investigate robustness context ML-based EDA...

Most vision-language (VL) trackers rely on coarse-grained information from sentences to achieve multi-modal alignment. However, this is insufficient for accurately describing the target in each frame due inherent ambiguity, summarization, and invariance of sentences, thereby making alignment challenging. This paper introduces TTCTrack, a novel VL tracker that employs textual token classification address challenge. Specifically, we exploit cross-relations classify tokens into various types...

We define integrals for functions on finite-dimensional algebras, adapting methods from Leinster's research. This paper discusses the relationships between of defined subsets $\mathbb{I}_1 \subseteq {\mathit{\Lambda}}_1$ and $\mathbb{I}_2 {\mathit{\Lambda}}_2$ two under influence a mapping $\omega$, which can be an injection or bijection. explore four specific cases: $\bullet$ $\omega$ as monotone non-decreasing right-continuous function; injective, absolutely continuous bijection; identity...

State-of-the-art (SOTA) visual object tracking methods have significantly enhanced the autonomy of unmanned aerial vehicles (UAVs). However, in low-light conditions, presence irregular real noise from environments severely degrades performance these SOTA methods. Moreover, existing denoising techniques often fail to meet real-time processing requirements when deployed as plug-and-play denoisers for UAV tracking. To address this challenge, work proposes a novel conditional generative denoiser...

Clear cell renal carcinoma (ccRCC) represents the most frequent form of (RCC), and accurate International Society Urological Pathology (ISUP) grading is crucial for prognosis treatment selection.This study presents a new deep network called Multi-scale Fusion Network (MsfNet), which aims to enhance automatic ISUP grade ccRCC with digital histopathology pathology images.The MsfNet overcomes limitations traditional ResNet50 by multi-scale information fusion dynamic allocation channel...

Crowd management research shows a lack of depth in the literature insofar as most major incidents can be prevented or minimized by proper strategy. Specifically, if abnormal crowd events detected early and relevant governing agency take appropriate actions towards mitigating dangers, accidental injury incident contained. This paper presents technical approach to gather required data using fixed cameras collect visual while grid model describe distribution. The measured area will divided into...

With the development of digital wireless communication technol-ogy, signal identification has been suffering from increasingly complex electromagnetic environment and higher spectrum utilization. In this paper, we propose a method based on interference cleaning convolutional neural network (CNN) in 230MHz Band. The firstly analyzes received time domain, building feature data sets combined with amplitudes, phases, in-phase components orthogonal components. then generalizes singular value...

Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to smaller learnable student network.Existing KD methods have mainly considered two types of knowledge, namely the individual and relational knowledge. However, these are usually modeled independently while inherent correlations between them largely ignored. It is critical for sufficient learning integrate both reserving their correlation. In this paper, we propose distill novel holistic...

Abstract Sensor fusion is a key technology in ADAS (FCW, PCW, AEB, ACC, etc.) In this paper, the target-level method used to realize recognition of targets through algorithms such as estimation driving area, screening for effective targets, matching, target and tracking. Simulation test real vehicle show that can identify steadily screen out radar jamming guardrails viaducts.

Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between a labeled source and an unlabeled target domain. However, these face challenges when dealing with Multivariate Time-Series (MTS) data. MTS data typically consist of multiple sensors, each its own unique distribution. This characteristic makes it hard to adapt existing UDA methods, which mainly focus on aligning global features while overlooking...